
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase     :	List[Any]       			=  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Union[str, Any]       			=  [
        'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BioGptForCausalLM',
        'BioGptForTokenClassification',
        'BioGptForSequenceClassification',
        'BioGptModel',
        'BioGptPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
    from .tokenization_biogpt import BioGptTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_biogpt import (
            BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BioGptForCausalLM,
            BioGptForSequenceClassification,
            BioGptForTokenClassification,
            BioGptModel,
            BioGptPreTrainedModel,
        )


else:
    import sys

    __lowerCAmelCase     :	str       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

